SoroushVahidi/scidocs huggingface dataset:
Monte Carlo localization in hand-drawn maps
(2015)

Robot localization is one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we can successfully identify the room in which the robot is located in 80% of the tests.
The Fast Bilateral Solver Supplement
(2015)

This supplement provides additional details on the bilateral solver and its extensions in Sections 1–4, and a plethora of experiments and comparisons to prior work in Section 5. In Section 1 we derive the bilateral-space quadratic objective at the core of the bilateral solver. Section 2 details the bilateral-space pyramid representation used for faster initialization and preconditioning. Section 3 extends the bilateral solver to use a robust error function to cope with outliers. Section 4 improves the performance of the bilateral solver when applied to multiple output channels. Finally, in Section 5 we provide additional evaluation of the bilateral solver and extensively compare it to numerous existing and novel baseline techniques.
A Framework for Emotion Mining from Text in Online Social Networks
(2010)

Online Social Networks are so popular nowadays that they are a major component of an individual’s social interaction. They are also emotionally-rich environments where close friends share their emotions, feelings and thoughts. In this paper, a new framework is proposed for characterizing emotional interactions in social networks, and then using these characteristics to distinguish friends from acquaintances. The goal is to extract the emotional content of texts in online social networks. The interest is in whether the text is an expression of the writer’s emotions or not. For this purpose, text mining techniques are performed on comments retrieved from a social network. The framework includes a model for data collection, database schemas, data processing and data mining steps. The informal language of online social networks is a main point to consider before performing any text mining techniques. This is why the framework includes the development of special lexicons. In general, the paper presents a new perspective for studying friendship relations and emotions’ expression in online social networks where it deals with the nature of these sites and the nature of the language used. It considers Lebanese Face book users as a case study. The technique adopted is unsupervised, it mainly uses the k-means clustering algorithm. Experiments show high accuracy for the model in both determining subjectivity of texts and predicting friendship.
Pseudo Random Pulse Driven Advanced In-Cell Touch Screen Panel for Spectrum Spread Electromagnetic Interference
(2018)

For the first time, we analyze the mechanism of electromagnetic interference (EMI) in an advanced in-cell touch (AIT) panel, which is a state-of-the-art in-cell touch screen panel technology. The AIT panel has stronger EMI generation at a specific frequency band (0.15 ~ 30 MHz) due to a load free driving (LFD) method. The LFD is adopted to overcome the structural vulnerability of in-cell touch panel, which incurs large parasitic capacitance due to the touch electrodes within the display panel. In order to overcome the EMI problem, we present a novel driving method using pseudo random pulse (PRP). The power spectrum of PRP is well spread over the frequency band, resulting in a lower EMI. We measured the EMI level with a near-field probe, and the proposed driving method shows the EMI reduction of 6.5 dB. This EMI reduction is accomplished without structural reconfiguration, as well as the touch performance remains the same as that before PRP is applied. Therefore, the proposed driving method can be utilized in the field of automotive, military, and aviation industries as a user interface which requires high touch performance and low EMI generation due to the peculiarities.
Design of a new Dual Rotor Radial Flux BLDC motor with Halbach array magnets for an electric vehicle
(2016)

This paper presents a novel topology of dual rotor radial flux-BLDC motor with Halbach array magnets as an efficient solution for drives. Energy efficient motors are the need of day for electric vehicles, industrial and consumer products. Especially this concept is arrived to cater for the requirements of electrical vehicle. Due to advent of high permeability, high saturation magnetic materials, high energy product permanent magnets, BLDC motors are suitable candidate for electric vehicle propulsion. Traditionally, outer rotor BLDC motor is a wide choice which is being used in commercially available electric vehicles. New topologies of BLDC motors are being proposed by research activities which aim at improvement of torque density of motors. Dual rotor brushless DC machine (DR-BLDC) is one of the consequences of such research and claimed to have very high torque density and power density. In this paper, a new topology of Dual Rotor Halbach (DR-HB) BLDC motor is proposed which is aimed to increase the torque density of motor. The complete design of DR-HB BLDC motor is carried out and compared with DR-BLDC of the same specifications and volume. The obtained performance characteristics of the proposed design are very much encouraging in terms of torque density.
Improved Fully Differential Analog Filters
(2007)

This paper proposes two novel design techniques that improve the performance of fully differential filters built by coupling two equal single-ended filters. These filters usually reach a high common-mode rejection ratio (CMRR) without using tightly matched passive components but may become unstable because of common-mode signals. Previously proposed techniques to solve these problems, based on resistor or capacitor T-networks, reduce the CMRR. The two techniques proposed overcome this tradeoff. The first technique improves the stability of the filter common-mode gain by adding damping resistors that do not modify the desired differential frequency response or the CMRR. The second technique, which is particularly valuable when a path for input bias currents must be provided, uses a resistor network to estimate the common-mode voltage of the input and uses it as common ground voltage for the filter.
MLH-IDS: A Multi-Level Hybrid Intrusion Detection Method
(2014)

With the growth of networked computers and associated applications, intrusion detection has become essential to keeping networks secure. A number of intrusion detection methods have been developed for protecting computers and networks using conventional statistical methods as well as data mining methods. Data mining methods for misuse and anomaly-based intrusion detection, usually encompass supervised, unsupervised and outlier methods. It is necessary that the capabilities of intrusion detection methods be updated with the creation of new attacks. This paper proposes a multi-level hybrid intrusion detection method that uses a combination of supervised, unsupervised and outlierbased methods for improving the efficiency of detection of new and old attacks. The method is evaluated with a captured real-time flow and packet dataset called the Tezpur University intrusion detection system (TUIDS) dataset, a distributed denial of service dataset, and the benchmark intrusion dataset called the knowledge discovery and data mining Cup 1999 dataset and the new version of KDD (NSL-KDD) dataset. Experimental results are compared with existing multi-level intrusion detection methods and other classifiers. The performance of our method is very good.
Comparisons of randomization and K-degree anonymization schemes for privacy preserving social network publishing
(2009)

Many applications of social networks require identity and/or relationship anonymity due to the sensitive, stigmatizing, or confidential nature of user identities and their behaviors. Recent work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random ids does not guarantee privacy since the identification of the nodes can be seriously jeopardized by applying background based attacks. In this paper, we investigate how well an edge based graph randomization approach can protect node identities and sensitive links. We quantify both identity disclosure and link disclosure when adversaries have one specific type of background knowledge (i.e., knowing the degrees of target individuals). We also conduct empirical comparisons with the recently proposed K-degree anonymization schemes in terms of both utility and risks of privacy disclosures.
Animal-assisted therapy with children suffering from insecure attachment due to abuse and neglect: a method to lower the risk of intergenerational transmission of abuse?
(2008)

Children suffering from insecure attachment due to severe abuse and/or neglect are often characterized by internal working models which, although perhaps adaptive within the original family situation, are inappropriate and maladaptive in other relationships and situations. Such children have a higher probability than the general population of becoming abusing or neglecting parents. Besides the usual goals of psychotherapy, an overall goal is to stop the cycle of abuse in which abused children may grow up to be abusing parents. Therapy with these children is complicated by their distrust in adults as well as difficulties in symbolization due to trauma during the preverbal stage. Animal-Assisted Therapy (AAT) provides avenues for circumventing these difficulties, as well as providing additional tools for reaching the inner world of the client. This article gives a brief background of the connection between insecure attachment and intergenerational transmission of abuse and neglect as well as a brief overview of the principles of AAT in a play therapy setting. A rationale for the use of AAT as a unique therapy technique for children having suffered from abuse and neglect is followed by a number of clinical examples illustrating AAT.
Adapting recommender systems to the requirements of personal health record systems
(2010)

In the future many people in industrialized countries will manage their personal health data electronically in centralized, reliable and trusted repositories - so-called personal health record systems (PHR). At this stage PHR systems still fail to satisfy the individual medical information needs of their users. Personalized recommendations could solve this problem.
 A first approach of integrating recommender system (RS) methodology into personal health records - termed health recommender system (HRS) - is presented. By exploitation of existing semantic networks like Wikipedia a health graph data structure is obtained. The data kept within such a graph represent health related concepts and are used to compute semantic distances among pairs of such concepts.
 A ranking procedure based on the health graph is outlined which enables a match between entries of a PHR system and health information artifacts. This way a PHR user will obtain individualized health information he might be interested in.
A proposed semantic machine translation system for translating Arabic text to Arabic sign language
(2011)

Arabic Sign Language (ArSL) is the native language for the Arabic deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express person thoughts instead of sounds. In recent years, interest in automatically translating text to sign language for different languages has increased. However, a small set of these works are specialized in ArSL. Basically, these works translate word by word without taking care of the semantics of the translated sentence or the translation rules of Arabic text to Arabic sign language. In this paper we will present a proposed system for translating Arabic text to Arabic sign language in the jurisprudence of prayer domain. The proposed system will translate Arabic text to ArSL by applying ArSL translation rules as well as using domain ontology.
Automatic image retargeting with fisheye-view warping
(2005)

Image retargeting is the problem of adapting images for display on devices different than originally intended. This paper presents a method for adapting large images, such as those taken with a digital camera, for a small display, such as a cellular telephone. The method uses a non-linear fisheye-view warp that emphasizes parts of an image while shrinking others. Like previous methods, fisheye-view warping uses image information, such as low-level salience and high-level object recognition to find important regions of the source image. However, unlike prior approaches, a non-linear image warping function emphasizes the important aspects of the image while retaining the surrounding context. The method has advantages in preserving information content, alerting the viewer to missing information and providing robustness.
A 7.6 mW, 414 fs RMS-Jitter 10 GHz Phase-Locked Loop for a 40 Gb/s Serial Link Transmitter Based on a Two-Stage Ring Oscillator in 65 nm CMOS
(2016)

This paper describes the design of a 10 GHz phase-locked loop (PLL) for a 40 Gb/s serial link transmitter (TX). A two-stage ring oscillator is used to provide a four-phase, 10 GHz clock for a quarter-rate TX. Several analyses and verification techniques, ranging from the clocking architectures for a 40 Gb/s TX to oscillation failures in a two-stage ring oscillator, are addressed in this paper. A tri-state-inverter-based frequency-divider and an AC-coupled clock-buffer are used for high-speed operations with minimal power and area overheads. The proposed 10 GHz PLL fabricated in the 65 nm CMOS technology occupies an active area of 0.009 mm2 with an integrated-RMS-jitter of 414 fs from 10 kHz to 100 MHz while consuming 7.6 mW from a 1.2-V supply. The resulting figure-of-merit is -238.8 dB, which surpasses that of the state-of-the-art ring-PLLs by 4 dB.
Fusion of VR and teleoperation for innovative near-presence laboratory experience in engineering education
(2017)

Distance learning is becoming a popular mode of learning among individuals seeking to add new skills to their repertoire or acquire an advanced degree. The availability of online course materials from institutions in academia aid this process, lending standardized and validated information to the audience seeking such knowledge. Although the online system of learning is extremely convenient for acquiring theoretical knowledge, the scope of hands-on experience is limited to the use of simulation software or involves the purchase of expensive laboratory kits. The proliferation of new technology like Virtual Reality (VR), Fifth Generation cellular networks (5G), and industrial robotics can be leveraged to bridge this gap, however. This will enable effective delivery of traditional disciplines like Electrical, Computer, or Mechanical Engineering using the online mode. This paper discusses the enabling technologies for implementing such a system. An ongoing effort to establish a proof-of-concept system in the Electrical and Computer Engineering department of the University of Nebraska-Lincoln is presented.
Discriminating IT Governance
(2015)

T information technology (IT) governance literature predominantly explains firms’ IT governance choices, but not their strategic consequences. We develop the idea that a firm’s IT governance choices induce adeptness at strategically exploiting IT only when they are discriminatingly aligned with its departments’ knowledge outside their specialty. Discriminating means that governing the two undertheorized classes of IT assets—apps and infrastructure—requires “peripheral” knowledge in different departments. Analyses of data from 105 firms support our middle-range theory.
Detecting malicious tweets in trending topics using a statistical analysis of language
(2013)

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.12.015 ⇑ Corresponding author. E-mail addresses: juaner@lsi.uned.es (J. MartinezAraujo). Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts. 2012 Elsevier Ltd. All rights reserved.
Enriching textbooks with images
(2011)

Textbooks have a direct bearing on the quality of education imparted to the students. Therefore, it is of paramount importance that the educational content of textbooks should provide rich learning experience to the students. Recent studies on understanding learning behavior suggest that the incorporation of digital visual material can greatly enhance learning. However, textbooks used in many developing regions are largely text-oriented and lack good visual material. We propose techniques for finding images from the web that are most relevant for augmenting a section of the textbook, while respecting the constraint that the same image is not repeated in different sections of the same chapter. We devise a rigorous formulation of the image assignment problem and present a polynomial time algorithm for solving the problem optimally. We also present two image mining algorithms that utilize orthogonal signals and hence obtain different sets of relevant images. Finally, we provide an ensembling algorithm for combining the assignments. To empirically evaluate our techniques, we use a corpus of high school textbooks in use in India. Our user study utilizing the Amazon Mechanical Turk platform indicates that the proposed techniques are able to obtain images that can help increase the understanding of the textbook material.
Balun and power divider based on multilayer ring resonators (MRR)
(2017)

This paper presents novel super compact microwave power dividers and balun (balanced-to-unbalanced) circuits. The proposed devices are based on multilayer ring resonators (MRR) structure. These new microwave devices are highly compact and flexible in design that can operate within various desirable bandwidths from narrow-band to ultrawideband (UWB), hence performing as bandpass filters simultaneously along with their own functions. It is also possible to have arbitrary power divisions. By this technique, a balun can be simply converted to a power diver and vice versa. Sample circuits are designed and the scattering characteristics are provided using electromagnetic simulation software. The dimensions of the devices are 2.3 mm χ 2.3 mm χ 1.5 mm.
Comparative Study on Generative Adversarial Networks
(2018)

In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been proposed in the last two years. We study the original model proposed by Goodfellow et al. (2014) as well as modifications over the original model and provide a comparative analysis of these models.
A Novel Deployment Scheme for Green Internet of Things
(2014)

The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment.
Background modelling based on generative unet
(2017)

Background Modelling is a crucial step in background/foreground detection which could be used in video analysis, such as surveillance, people counting, face detection and pose estimation. Most methods need to choose the hyper parameters manually or use ground truth background masks (GT). In this work, we present an unsupervised deep background (BG) modelling method called BM-Unet which is based on a generative architecture that given a certain frame as input it generates as output the corresponding background image — to be more precise, a probabilistic heat map of the colour values. Our method learns parameters automatically and an augmented version of it that utilises colour, intensity differences and optical flow between a reference and a target frame is robust to rapid illumination changes and camera jitter. Besides, it can be used on a new video sequence without the need of ground truth background/foreground masks for training. Experiment evaluations on challenging sequences in SBMnet data set demonstrate promising results over state-of-the-art methods.